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기체 누출 사전 감지를 위한 LSTM 오토인코더 기반 이상 진단 모델 설계 및 구현
김영선,김민,알폰,박연경 한국신뢰성학회 2024 신뢰성응용연구 Vol.24 No.1
Purpose: This study proposes a gas-absorbing device with high reliability and availability. To detect anomalies in the device, we applied an anomaly-detection algorithm based on artificial intelligence. Methods: We used an LSTM-based autoencoder model to train normal data, and the model was processed to detect anomalies when the mean average error (MAE) deviated more than three times the standard deviation of its mean. To handle false alarms, we introduced a method that assigns increment and decrement scores for all anomaly and normal points and diagnoses an anomaly point when the accumulated score exceeds a designated threshold. Results: The model could diagnose anomalies approximately four months before failure, and it could correctly diagnose a return-to-normal after repair. Conclusion: This algorithm is suitable for systems that are employed in environments that must operate continuously every day.